Literature DB >> 2379824

Mathematical models of gene amplification with applications to cellular drug resistance and tumorigenicity.

M Kimmel1, D E Axelrod.   

Abstract

An increased number of copies of specific genes may offer an advantage to cells when they grow in restrictive conditions such as in the presence of toxic drugs, or in a tumor. Three mathematical models of gene amplification and deamplification are proposed to describe the kinetics of unstable phenotypes of cells with amplified genes. The models differ in details but all assume probabilistic mechanisms of increase and decrease in gene copy number per cell (gene amplification/deamplification). Analysis of the models indicates that a stable distribution of numbers of copies of genes per cell, observed experimentally, exists only if the probability of deamplification exceeds the probability of amplification. The models are fitted to published data on the loss of methotrexate resistance in cultured cell lines, due to the loss of amplified dihydrofolate reductase gene. For two mouse cell lines unstably resistant to methotrexate the probabilities of amplification and deamplification of the dihydrofolate reductase gene on double minute chromosomes are estimated to be approximately 2% and 10%, respectively. These probabilities are much higher than widely presumed. The models explain the gradual disappearance of the resistant phenotype when selective pressure is withdrawn, by postulating that the rate of deamplification exceeds the rate of amplification. Thus it is not necessary to invoke a growth advantage of nonresistant cells which has been the standard explanation. For another analogous process, the loss of double minute chromosomes containing the myc oncogene from SEWA tumor cells, the growth advantage model does seem to be superior to the amplification and deamplification model. In a more theoretical section of the paper, it is demonstrated that gene amplification/deamplification can result in reduction to homozygosity, such as is observed in some tumors. Other applications are discussed.

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Year:  1990        PMID: 2379824      PMCID: PMC1204089     

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  24 in total

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Authors:  R P Novick; F C Hoppensteadt
Journal:  Plasmid       Date:  1978-09       Impact factor: 3.466

2.  Some stochastic models for plasmid copy number.

Authors:  E Seneta; S Tavaré
Journal:  Theor Popul Biol       Date:  1983-04       Impact factor: 1.570

3.  Gene amplification, drug resistance, and cancer.

Authors:  R T Schimke
Journal:  Cancer Res       Date:  1984-05       Impact factor: 12.701

4.  Rapid emergence of methotrexate resistance in cultured mouse cells.

Authors:  H Rath; T Tlsty; R T Schimke
Journal:  Cancer Res       Date:  1984-08       Impact factor: 12.701

5.  Enhancement of methotrexate resistance and dihydrofolate reductase gene amplification by treatment of mouse 3T6 cells with hydroxyurea.

Authors:  P C Brown; T D Tlsty; R T Schimke
Journal:  Mol Cell Biol       Date:  1983-06       Impact factor: 4.272

6.  A stochastic model for cellular senescence. Part I. Theoretical considerations.

Authors:  R B Jones; C K Lumpkin; J R Smith
Journal:  J Theor Biol       Date:  1980-10-07       Impact factor: 2.691

7.  Analysis of variability in albumin content of sister hepatoma cells and a model for geometric phenotypic variability (quantitative shift model).

Authors:  J A Peterson
Journal:  Somat Cell Mol Genet       Date:  1984-07

8.  The widespread nature of phenotypic variability in hepatomas and cell lines, in the form of a geometric series.

Authors:  J A Peterson
Journal:  J Theor Biol       Date:  1983-05-07       Impact factor: 2.691

9.  Relationship of amplified dihydrofolate reductase genes to double minute chromosomes in unstably resistant mouse fibroblast cell lines.

Authors:  P C Brown; S M Beverley; R T Schimke
Journal:  Mol Cell Biol       Date:  1981-12       Impact factor: 4.272

10.  UV radiation facilitates methotrexate resistance and amplification of the dihydrofolate reductase gene in cultured 3T6 mouse cells.

Authors:  T D Tlsty; P C Brown; R T Schimke
Journal:  Mol Cell Biol       Date:  1984-06       Impact factor: 4.272

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  14 in total

Review 1.  Auto-catalysed progression of aneuploidy explains the Hayflick limit of cultured cells, carcinogen-induced tumours in mice, and the age distribution of human cancer.

Authors:  D Rasnick
Journal:  Biochem J       Date:  2000-06-15       Impact factor: 3.857

2.  An elementary approach to modeling drug resistance in cancer.

Authors:  Cristian Tomasetti; Doron Levy
Journal:  Math Biosci Eng       Date:  2010-10       Impact factor: 2.080

3.  On the probability of random genetic mutations for various types of tumor growth.

Authors:  Cristian Tomasetti
Journal:  Bull Math Biol       Date:  2012-02-07       Impact factor: 1.758

4.  Time-continuous branching walk models of unstable gene amplification.

Authors:  M Kimmel; D N Stivers
Journal:  Bull Math Biol       Date:  1994-03       Impact factor: 1.758

Review 5.  Tumor evolution: Linear, branching, neutral or punctuated?

Authors:  Alexander Davis; Ruli Gao; Nicholas Navin
Journal:  Biochim Biophys Acta Rev Cancer       Date:  2017-01-19       Impact factor: 10.680

6.  Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response.

Authors:  Nicholas J Szerlip; Alicia Pedraza; Debyani Chakravarty; Mohammad Azim; Jeremy McGuire; Yuqiang Fang; Tatsuya Ozawa; Eric C Holland; Jason T Huse; Suresh Jhanwar; Margaret A Leversha; Tom Mikkelsen; Cameron W Brennan
Journal:  Proc Natl Acad Sci U S A       Date:  2012-02-08       Impact factor: 11.205

7.  The impact of cell density and mutations in a model of multidrug resistance in solid tumors.

Authors:  James Greene; Orit Lavi; Michael M Gottesman; Doron Levy
Journal:  Bull Math Biol       Date:  2014-02-20       Impact factor: 1.758

Review 8.  Mathematical modeling as a tool for planning anticancer therapy.

Authors:  Andrzej Swierniak; Marek Kimmel; Jaroslaw Smieja
Journal:  Eur J Pharmacol       Date:  2009-10-13       Impact factor: 4.432

9.  Generalized principles of stochasticity can be used to control dynamic heterogeneity.

Authors:  David Liao; Luis Estévez-Salmerón; Thea D Tlsty
Journal:  Phys Biol       Date:  2012-11-29       Impact factor: 2.583

10.  A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

Authors:  Wen Juan Mo; Xu Ping Fu; Xiao Tian Han; Guang Yuan Yang; Ji Gang Zhang; Feng Hua Guo; Yan Huang; Yu Min Mao; Yao Li; Yi Xie
Journal:  BMC Genomics       Date:  2009-07-29       Impact factor: 3.969

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